Data processing in a large-scale, enterprise application often presents usability, manageability, and scalability problems due to the large volume of data. For example, Web sites generate gigabytes of data every day to describe actions made by visitors to the sites. In fact, the average number of hits on a popular network of Web sites can reach 1.5 billion hits per day or more. This data has several dimensions, such as where each visitor came from, the time of day, the route taken through the site, and the like. Moreover, the amount of data continually increases as the number of Web services and the amount of business they conduct increases. Therefore, processing the large amount of data to produce meaningful usage reports and clickstream analysis for a network of sites involves overcoming several challenges.
Some prior art systems facilitate the transfer of data between a requesting computer and data repository over a computer network. However, such systems lack a framework for providing flexible reporting, scalability, fast response times, and intelligent caching. In addition, such systems fail to operate efficiently with the enormous size of current databases. Other prior art systems address database design, storage, and access techniques. However, such prior art systems fail to provide a system for producing combined reports having data accessed from various heterogeneous databases.
For these reasons, a feature-rich and scalable reporting information service is desired to address one or more of these and other disadvantages.